polypack (Python)

Python bindings for the polypack embedded property-graph and vector-search core. PolyGraph and GraphQuery are a Python layer mirroring the TypeScript PolyGraph/GraphQuery API; vector indexing, persisted queries, and directory-backed persistence run in the Rust core (polypack._core, built from crates/polypack-python). Simple hot-cache filters, ordering, pagination, and aggregates use the local Python pipeline to avoid repeatedly serializing the working set across the Python/Rust boundary.

Install

pip install polypack-db

Requires Python 3.8+ and numpy. Prebuilt wheels are published from polypack-db on PyPI; the version tracks the TypeScript package and polypack-core crate in lockstep.

Quick start

import time
from polypack import PolyGraph

graph = PolyGraph()

graph.add_node({
    "id": "doc_1",
    "type": "document",
    "data": {"title": "Quantum Computing"},
    "vector": [0.95, 0.20, 0.10],
    "insertedAt": int(time.time() * 1000),
    "updatedAt": int(time.time() * 1000),
})

graph.add_edge("doc_1", "REFERENCES", "doc_2", ownership="owned")

# Search by similarity
graph.query() \
    .where_type("document") \
    .similar_to([0.90, 0.30, 0.10], 0.5, 5) \
    .to_list()

# Filter, traverse, aggregate
graph.query().where("title", "Quantum Computing").traverse("REFERENCES", 3).to_list()
graph.query().where_type("book").aggregate("price", "avg")

For directory-backed stores, use graph.query_persisted() for filters, ordering, pagination, and counts. These operations execute in the native store and only materialize the requested result page in Python:

graph.query_persisted().where_type("book").order_by("price", "desc").limit(20).to_list()

query_persisted() requires an open directory-backed store and returns a PersistedGraphQuery. Its supported chainable methods are:

• where_type(*types)
• where(field, value)
• where_range(field, above=None, below=None) with exclusive boundaries
• order_by(field, direction="asc")
• offset(n) and limit(n)

Its terminal methods are synchronous: to_list(), ids(), and count(). to_list() returns only the requested page, while count() counts all records matching the filters before pagination. Pending writable changes are saved before a persisted query runs. Similarity, joins, and traversal remain on the hot GraphQuery API.

Python API surface

The main public classes are:

• PolyGraph: graph lifecycle, mutations, transactions, persistence, and hot queries.
• GraphQuery: synchronous hot-cache filters, ranges, ordering, pagination, traversal, joins, similarity, activation, and aggregates.
• PersistedGraphQuery: native storage-level filtering, ordering, pagination, and counting for directory-backed stores.
• ExactIndex and HnswIndex: standalone native vector indexes.
• ActivationEngine: adaptive-memory operations over a PolyGraph.

Python method names use snake_case equivalents of the TypeScript API, such as add_node, update_node, where_type, similar_to, and to_list.

Persistence

PolyGraph.open(directory) attaches a directory-backed binary store (snapshot + write-ahead log, shared format with the TypeScript BinaryStoreAdapter) and loads any existing state. Use it as a context manager so the store is compacted and closed on exit:

with PolyGraph.open("./data") as graph:
    graph.add_node({...})
    graph.save()  # persists queued puts and deletions

Activation (adaptive memory)

Nodes can carry durable activation state that persists through the store. Reinforcement decay-corrects the prior state, adds a delta, and re-anchors the decay clock; reads decay lazily as a pure function of elapsed time:

from polypack import PolyGraph, ActivationEngine

graph = PolyGraph()
graph.add_node({"id": "a", "type": "doc", "data": {}, "insertedAt": 1, "updatedAt": 1})
graph.reinforce_node("a", 0.6, "user_read")      # durable, persisted
graph.reinforce_node("a", 0.3, "user_read", context="project-x")  # also reinforces a context
graph.suppress_node("outdated", 1.0, "stale")    # durable inhibition
graph.get_activation("a")                         # ~0.6, decay-corrected
graph.top_activated(5)                            # working-memory primitive

engine = ActivationEngine(graph)
engine.bump_attention("a", 0.2)                  # local, never serialized
engine.spread(["a"], depth=2, decay=0.5)         # relational spreading activation
engine.pulse([1.0, 0.0, ...])                    # semantic region scoring (vector)
engine.absorb([1.0, 0.0, ...])                   # pulse + reinforce above threshold
engine.working_memory(5)                         # current "mental state"
engine.working_memory(limit=8, token_budget=2000, diversity_lambda=0.5)  # budgeted + diverse
engine.record_feedback("a", was_useful=True)     # nudge pulse's scoring weights

graph.query().where_activated(0.4) and graph.query().order_by_activation() filter/order by current activation. merge_activation and decay_factor are exposed for parity with the TypeScript/Rust implementations.

Nodes also support memory classes (episodic/semantic/procedural/entity, set via register_node_type(type, memory_class=...) as a type default or memoryClass on the node itself, driving differentiated decay half-lives) and confidence/provenance fields (confidence, source, observedAt, derivedFrom, supersedes, contradicts) — ordinary node fields, not activation state. graph.supersede(id, superseded_id) records a contradiction (suppresses the superseded node without deleting it) and graph.consolidate(node, source_ids) promotes a group of nodes into a higher-level one, both while keeping the sources in the graph. See docs/API.md for full parameter details.

save() writes the complete current graph and flushes any deletions recorded since the last save. close_store() — and exiting the with block, which calls it — saves any unsaved changes first, then compacts and closes; you don't need to call save() yourself before it, though doing so periodically (as above) still bounds how much a single close has to write and how much a crash before that could lose.

Vector indexes

ExactIndex and HnswIndex wrap the Rust vector core directly and can be used standalone, independent of PolyGraph:

from polypack import ExactIndex, HnswIndex

index = ExactIndex(distance="cosine")
index.add("doc_1", [0.95, 0.20, 0.10])
index.query([0.90, 0.30, 0.10], top_k=5, threshold=0.5)

approx = HnswIndex(m=16, ef_search=200)
approx.add_many([("doc_1", [0.95, 0.20, 0.10]), ("doc_2", [0.10, 0.90, 0.05])])

Errors

Native errors surface as PolypackError subclasses: PolypackValueError, PolypackDimensionError, PolypackClosedError, PolypackVersionError, PolypackCorruptDataError, and PolypackStorageError.

Development

See the top-level CONTRIBUTING.md for building the native extension with maturin and running the Python test suite.